# Online terrain estimation for autonomous vehicles on deformable terrains

## TL;DR

This paper presents a real-time terrain estimation framework for autonomous vehicles on deformable terrains, using a reduced-order nonlinear terramechanics model and an unscented Kalman filter to improve navigation accuracy.

## Contribution

It introduces a computationally efficient surrogate terramechanics model extending the Bekker model and demonstrates its effectiveness in real-time sinkage parameter estimation.

## Key findings

- Estimated sinkage exponent within 4% of true value for clay and sandy loam.
- Reduced prediction errors of vehicle states by orders of magnitude using the estimated parameters.
- Surrogate model reduces computation cost by an order of magnitude compared to traditional SCM.

## Abstract

In this work, a terrain estimation framework is developed for autonomous vehicles operating on deformable terrains. Previous work in this area usually relies on steady state tire operation, linearized classical terramechanics models, or on computationally expensive algorithms that are not suitable for real-time estimation. To address these shortcomings, this work develops a reduced-order nonlinear terramechanics model as a surrogate of the Soil Contact Model (SCM) through extending a state-of-the-art Bekker model to account for additional dynamic effects. It is shown that this reduced-order surrogate model is able to accurately replicate the forces predicted by the SCM while reducing the computation cost by an order of magnitude. This surrogate model is then utilized in a unscented Kalman filter to estimate the sinkage exponent. Simulations suggest this parameter can be estimated within 4% of its true value for clay and sandy loam terrains. It is also shown that utilizing this estimated parameter can reduce the prediction errors of the future vehicle states by orders of magnitude, which could assist with achieving more robust model-predictive autonomous navigation strategies.